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https://hdl.handle.net/1959.11/43100
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Abdollahi, Abolfazl | en |
dc.contributor.author | Pradhan, Biswajeet | en |
dc.contributor.author | Shukla, Nagesh | en |
dc.contributor.author | Chakraborty, Subrata | en |
dc.contributor.author | Alamri, Adbullah | en |
dc.date.accessioned | 2022-02-21T22:42:44Z | - |
dc.date.available | 2022-02-21T22:42:44Z | - |
dc.date.issued | 2020-05-02 | - |
dc.identifier.citation | Remote Sensing, 12(9), p. 1-22 | en |
dc.identifier.issn | 2072-4292 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/43100 | - |
dc.description.abstract | One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively. | en |
dc.language | en | en |
dc.publisher | MDPI AG | en |
dc.relation.ispartof | Remote Sensing | en |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.3390/rs12091444 | en |
dcterms.accessRights | UNE Green | en |
local.contributor.firstname | Abolfazl | en |
local.contributor.firstname | Biswajeet | en |
local.contributor.firstname | Nagesh | en |
local.contributor.firstname | Subrata | en |
local.contributor.firstname | Adbullah | en |
local.profile.school | School of Science and Technology | en |
local.profile.email | schakra3@une.edu.au | en |
local.output.category | C1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.publisher.place | Switzerland | en |
local.identifier.runningnumber | 1444 | en |
local.format.startpage | 1 | en |
local.format.endpage | 22 | en |
local.identifier.scopusid | 85085972386 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 12 | en |
local.identifier.issue | 9 | en |
local.title.subtitle | A State-Of-The-Art Review | en |
local.access.fulltext | Yes | en |
local.contributor.lastname | Abdollahi | en |
local.contributor.lastname | Pradhan | en |
local.contributor.lastname | Shukla | en |
local.contributor.lastname | Chakraborty | en |
local.contributor.lastname | Alamri | en |
dc.identifier.staff | une-id:schakra3 | en |
local.profile.orcid | 0000-0002-0102-5424 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:1959.11/43100 | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction | en |
local.relation.fundingsourcenote | This research is supported by the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, the University of Technology Sydney (UTS). This research was also supported by Researchers Supporting Project number RSP-2019/14, King Saud University, Riyadh, Saudi Arabia. | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Abdollahi, Abolfazl | en |
local.search.author | Pradhan, Biswajeet | en |
local.search.author | Shukla, Nagesh | en |
local.search.author | Chakraborty, Subrata | en |
local.search.author | Alamri, Adbullah | en |
local.open.fileurl | https://rune.une.edu.au/web/retrieve/77807a58-d1a9-4ebb-9e1e-ae1f6cb3f687 | en |
local.uneassociation | No | en |
local.atsiresearch | No | en |
local.sensitive.cultural | No | en |
local.year.published | 2020 | en |
local.fileurl.open | https://rune.une.edu.au/web/retrieve/77807a58-d1a9-4ebb-9e1e-ae1f6cb3f687 | en |
local.fileurl.openpublished | https://rune.une.edu.au/web/retrieve/77807a58-d1a9-4ebb-9e1e-ae1f6cb3f687 | en |
local.subject.for2020 | 460106 Spatial data and applications | en |
local.subject.for2020 | 460306 Image processing | en |
local.subject.for2020 | 461103 Deep learning | en |
local.subject.seo2020 | 280115 Expanding knowledge in the information and computing sciences | en |
Appears in Collections: | Journal Article School of Science and Technology |
Files in This Item:
File | Description | Size | Format | |
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openpublished/DeepChakraborty2020JournalArticle.pdf | Published version | 3.39 MB | Adobe PDF Download Adobe | View/Open |
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